Finance

Calculate Average Revenue per Active Customer in Redshift

Making the most of your
AWS Redshift
data
February 22, 2022

What is Average Revenue per Active Customer?

Average Revenue per Active Customer (ARPA) is a metric that looks at the average revenue generated by a customer over a certain time period.

ARPA is calculated by dividing the total revenue generated by a company by the number of active customers during that time period.

For example, if a company has 100 customers and generates $1,000,000 in revenue during a certain time period, their ARPA is $10,000.

ARPA is a great metric to use as a benchmark for your company's growth. If you're looking to grow your company, you need to be able to show that you're increasing your ARPA.

For example, if you have 100 customers and you're generating $1,000,000 in revenue, your ARPA is $10,000. If you have 200 customers and you're generating $2,000,000 in revenue, your ARPA is $10,000.

This means that you're generating the same amount of revenue per customer, but you have twice as many customers.

How do you calculate Average Revenue per Active Customer in AWS Redshift?

It can be difficult to calculate Average Revenue per Active Customer directly inside of AWS Redshift; that's where Causal comes in.

Causal is a modelling tool which lets you build models on top of your AWS Redshift data. You simply connect Causal to your AWS Redshift account, and then you can build formulae in Causal to calculate your Average Revenue per Active Customer.

What is Causal?

Causal lets you build models effortlessly and share them with interactive, visual dashboards that everyone will understand.

In Causal, you build your models out of variables, which you can then link together in simple plain-English formulae to calculate metrics like Average Revenue per Active Customer. This makes your models easy to understand and quick to build, so you can spend minutes, not days, on your models.

A comparison of formulae in Excel and Causal

When you're done, you can share the link to your model with stakeholders. They'll be able to view your model's outputs in a visual dashboard, rather than a jumble of tabs and complex formulae. The dashboards are interactive, letting viewers tweak your assumptions to see how they affect the model's outputs.

Causal lets you add visuals in a single click, letting you plot out graphs and distributions for metrics like Average Revenue per Active Customer.

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How to Calculate Average Revenue per Active Customer in Redshift

Making the most of your AWS Redshift data

What is Average Revenue per Active Customer?

Average Revenue per Active Customer (ARPA) is a metric that looks at the average revenue generated by a customer over a certain time period.

ARPA is calculated by dividing the total revenue generated by a company by the number of active customers during that time period.

For example, if a company has 100 customers and generates $1,000,000 in revenue during a certain time period, their ARPA is $10,000.

ARPA is a great metric to use as a benchmark for your company's growth. If you're looking to grow your company, you need to be able to show that you're increasing your ARPA.

For example, if you have 100 customers and you're generating $1,000,000 in revenue, your ARPA is $10,000. If you have 200 customers and you're generating $2,000,000 in revenue, your ARPA is $10,000.

This means that you're generating the same amount of revenue per customer, but you have twice as many customers.

How do you calculate Average Revenue per Active Customer in AWS Redshift?

It can be difficult to calculate Average Revenue per Active Customer directly inside of AWS Redshift; that's where Causal comes in.

Causal is a modelling tool which lets you build models on top of your AWS Redshift data. You simply connect Causal to your AWS Redshift account, and then you can build formulae in Causal to calculate your Average Revenue per Active Customer.

What is Causal?

Causal lets you build models effortlessly and share them with interactive, visual dashboards that everyone will understand.

In Causal, you build your models out of variables, which you can then link together in simple plain-English formulae to calculate metrics like Average Revenue per Active Customer. This makes your models easy to understand and quick to build, so you can spend minutes, not days, on your models.

A comparison of formulae in Excel and Causal

When you're done, you can share the link to your model with stakeholders. They'll be able to view your model's outputs in a visual dashboard, rather than a jumble of tabs and complex formulae. The dashboards are interactive, letting viewers tweak your assumptions to see how they affect the model's outputs.

Causal lets you add visuals in a single click, letting you plot out graphs and distributions for metrics like Average Revenue per Active Customer.

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